Maximum Resilience of Artificial Neural Networks

The deployment of Artificial Neural Networks (ANNs) in safety-critical applications poses a number of new verification and certification challenges. In particular, for ANN-enabled self-driving vehicles it is important to establish properties about the resilience of ANNs to noisy or even maliciously manipulated sensory input. We are addressing these challenges by defining resilience properties of ANN-based classifiers as the maximal amount of input or sensor perturbation which is still tolerated. This problem of computing maximal perturbation bounds for ANNs is then reduced to solving mixed integer optimization problems (MIP). A number of MIP encoding heuristics are developed for drastically reducing MIP-solver runtimes, and using parallelization of MIP-solvers results in an almost linear speed-up in the number (up to a certain limit) of computing cores in our experiments. We demonstrate the effectiveness and scalability of our approach by means of computing maximal resilience bounds for a number of ANN benchmark sets ranging from typical image recognition scenarios to the autonomous maneuvering of robots.Comment: Timestamp research work conducted in the project. version 2: fix some typos, rephrase the definition, and add some more existing wor

full-FORCE: A Target-Based Method for Training Recurrent Networks

Trained recurrent networks are powerful tools for modeling dynamic neural computations. We present a target-based method for modifying the full connectivity matrix of a recurrent network to train it to perform tasks involving temporally complex input/output transformations. The method introduces a second network during training to provide suitable "target" dynamics useful for performing the task. Because it exploits the full recurrent connectivity, the method produces networks that perform tasks with fewer neurons and greater noise robustness than traditional least-squares (FORCE) approaches. In addition, we show how introducing additional input signals into the target-generating network, which act as task hints, greatly extends the range of tasks that can be learned and provides control over the complexity and nature of the dynamics of the trained, task-performing network.Comment: 20 pages, 8 figure

EFFICIENCY AND PRODUCTIVITY GROWTH IN INDIAN BANKING

This paper attempts to examine technical efficiency and productivity performance of Indian scheduled commercial banks, for the period 1979-2008. We model a multiple output/multiple input technology production frontier using semiparametric estimation methods. The endogenity of multiple outputs is addressed by semi parametric estimates in part by introducing multivariate kernel estimators for the joint distribution of the multiple outputs and correlated random effects. Output is measured as the rupee value of total loans and total investments at the end of the year. The estimates provide robust inferences of the productivity and efficiency gains due to economic reforms.Banking, Frontier efficiency, Productivity

Sumoylation silences the plasma membrane leak K+ channel K2P1.

Reversible, covalent modification with small ubiquitin-related modifier proteins (SUMOs) is known to mediate nuclear import/export and activity of transcription factors. Here, the SUMO pathway is shown to operate at the plasma membrane to control ion channel function. SUMO-conjugating enzyme is seen to be resident in plasma membrane, to assemble with K2P1, and to modify K2P1 lysine 274. K2P1 had not previously shown function despite mRNA expression in heart, brain, and kidney and sequence features like other two-P loop K+ leak (K2P) pores that control activity of excitable cells. Removal of the peptide adduct by SUMO protease reveals K2P1 to be a K+-selective, pH-sensitive, openly rectifying channel regulated by reversible peptide linkage

Challenges in video based object detection in maritime scenario using computer vision

This paper discusses the technical challenges in maritime image processing and machine vision problems for video streams generated by cameras. Even well documented problems of horizon detection and registration of frames in a video are very challenging in maritime scenarios. More advanced problems of background subtraction and object detection in video streams are very challenging. Challenges arising from the dynamic nature of the background, unavailability of static cues, presence of small objects at distant backgrounds, illumination effects, all contribute to the challenges as discussed here

A novel correlation model for horizontal axis wind turbines operating at high-interference flow regimes

Driven by economics-of-scale factors, wind-turbine rotor sizes have increased formidably in recent years. Larger rotors with lighter blades of increased flexibility will experiment substantially higher levels of deformation. Future turbines will also incorporate advanced control strategies to widen the range of wind velocities over which energy is captured. These factors will extend turbine operational regimes, including flow states with high interference factors. In this paper we derive a new empirical relation to both improve and extend the range of Blade Element Momentum (BEM) models, when applied to high interference-factor regimes. In most BEM models, these flow regimes are modeled using empirical relations derived from experimental data. However, an empirical relation that best represents these flow states is still missing. The new relation presented in this paper is based on data from numerical experiments performed on an actuator disk model, and implemented in the context of a novel model of the BEM family called the DRD-BEM (Dynamic Rotor Deformation—BEM), recently introduced in Ponta, et al., 2016. A detailed description of the numerical experiments is presented, followed by DRD-BEM simulation results for the case of the benchmark NREL-5MW Reference Wind Turbine with this new polynomial curve incorporated